The role of admixture in human evolution

项目总结/摘要 人口混合的程度和相关性一直是最热门的问题之一， 讨论人类基因组学的问题最近的研究表明， 人口是人类进化的一个反复出现的特征。然而，尽管混合物在 在人类中，它通常不会在自然选择的群体基因组研究中被考虑。该项目将 利用古代和现代人类遗传变异数据以及重建的古代微生物组， 病原体，以了解人类种群之间混合的进化后果。首先 该项目将开发一种新的统计方法，可以检测遗传适应的基因组足迹 （选择性扫描），以及估计选择系数和有益等位基因 弹道这将使我们能够准确地量化和利用不同时间的适应签名 样本和调查混杂效应的过去的混合物选择签名在现代样本。 此外，我们将对比一个模型的同质选择与模型的激烈选择在出- 非洲（OoA）移民事件，以探讨是否适应寒冷的欧洲环境可能是一个 OoA扩展的先决条件。其次，我们将评估用于检测古旧语的方法的鲁棒性。 适应性基因渗入现代人类数据，通过检查被动遗传的替代假设， 古代基因片段与起源于现代人类的突变的搭便车。这将使更多的 准确解释现代遗传变异数据中的渐渗特征。第三，我们将测序 来自古代黎凡特的考古骨骼样本，这些样本与一个重大的历史事件有关， 青铜时代的崩溃，其特征是大规模的移民和文化更替。本研究将 提供了重要的见解，在何种程度上这样一个主要的混合物事件与变化 在人口规模、微生物组的健康相关参数以及新病原体的出现方面。 最后，我们将开发一种新的选择推理方法的基础上，现代混合种群的 祖先进化图（ARG）。使用ARG进行群体遗传推断有望提高 与以前的方法相比，这将使我们能够准确地估计选择的开始。 选择开始的知识有助于我们理解在没有选择的情况下可能的选择压力。 祖先混合前种群的古老DNA是可用的。总而言之，这项资助的启示 将对我们理解人类适应性的进化起源和健康影响至关重要 历史上的人口混合。我们将开发的新方法将导致前所未有的水平 关于基因组片段和突变的起源以及它们何时以及为什么被 选定.这些见解将提高我们解释个体基因组渗入和混合的能力 基因组图谱，从而有助于个性化基因组医学的进步。

PROJECT SUMMARY / ABSTRACT The extent and relevance of admixture between human populations has remained one of the most hotly debated questions in human genomics. Recent studies have revealed that admixture between diverged populations has been a recurring feature of human evolution. However, despite the ubiquity of admixture in humans, it is typically not accounted for in population genomic studies of natural selection. This project will utilize ancient and modern human genetic variation data as well as reconstructed ancient microbiomes and pathogens to learn about the evolutionary consequences of mixing between human populations. First, this project will develop a novel statistical method that can detect genomic footprints of genetic adaptation (selective sweeps) in admixed ancient DNA, as well as estimate the selection coefficient and beneficial allele trajectory. This will allow us to accurately quantify and utilize adaptation signatures across different temporal samples and investigate the confounding effect of past admixture on selection signatures in modern samples. Further, we will contrast a model of homogeneous selection with a model of intense selection during the Out- of-Africa (OoA) migration event to explore if adaptation to colder European environments might have been a prerequisite to the OoA expansion. Second, we will evaluate the robustness of methods for detecting archaic adaptive introgression in modern human data, by examining an alternative hypothesis of passive genetic hitchhiking of archaic segments with mutations that originated in modern humans. This will allow a more accurate interpretation of introgression signatures in modern genetic variation data. Third, we will sequence archaeological skeletal samples from the ancient Levant that are bracketing a major historical event, the Late Bronze Age Collapse, which is characterized by extensive migration and cultural turnover. This study will provide important insights into the extent to which such a major admixture event was associated with changes in population size, health-related parameters of the microbiome, and the appearance of novel pathogens. Finally, we will develop a novel selection inference approach for modern admixed populations based on the Ancestral Recombination Graph (ARG). Using the ARG for population genetic inference promises to boost power compared to previous approaches and will allow us to accurately estimate the onset of selection. Knowledge of the onset of selection aids our understanding of possible selective pressures in cases where no ancient DNA of an ancestral pre-admixture population is available. In conclusion, the insights from this grant will be critical to our understanding of the evolutionary origin of human adaptations and the health implications of historical population admixture. The novel methods that we will develop will lead to an unprecedented level of resolution regarding the origin of genomic segments and mutations and when and why they had been selected. These insights will increase our ability to interpret individual genomic introgression and admixture profiles and thus contribute to the advancement of personalized genomic medicine.